Albert Dominguez Mantes

Specialized signaling centers orchestrate robust development and regeneration. Limb morphogenesis, for instance, requires interactions between the mesoderm and the signaling center apical-ectodermal ridge (AER), whose properties and role in cell fate decisions have remained challenging to dissect. To tackle this, we developed mouse embryonic stem cell (mESC)–based heterogeneous cultures and a three-dimensional (3D) organoid model, termed budoids, comprising cells with AER, surface ectoderm, and mesoderm properties. mESCs were first induced into heterogeneous cultures that self-organized into domes in 2D. Aggregating these cultures formed mesodermal organoids with certain limb bud–like features in 3D, exhibiting chondrogenesis-based symmetry breaking and elongation. Using our organoids and quantitative in situ expression profiling, we uncovered that AER-like cells support nearby limb mesoderm and fibroblast identities while enhancing tissue polarization that permits distant cartilage formation. Together, our findings provide a powerful model to study epithelial signaling center-mesoderm interactions during morphogenesis and reveal the ability of signaling center AER cells to concurrently modulate cell fate and spatial organization.

Embryo development entails the formation of anatomical structures with distinct biochemical compositions. Compared with the wealth of knowledge on gene regulation, our understanding of metabolic programs operating during embryogenesis is limited. Mass spectrometry imaging (MSI) has the potential to map the distribution of metabolites across embryo development. Here we established uMAIA, an analytical framework for the joint analysis of large MSI datasets, which enables the construction of multidimensional metabolomic atlases. Employing this framework, we mapped the four-dimensional (4D) distribution of over a hundred lipids at micrometric resolution in Danio rerio embryos. We discovered metabolic trajectories that unfold in concert with morphogenesis and revealed spatially organized biochemical coordination overlooked by bulk measurements. Interestingly, lipid mapping revealed unexpected distributions of sphingolipid and triglyceride species, suggesting their involvement in pattern establishment and organ development. Our approach empowers a new generation of whole-organism metabolomic atlases and enables the discovery of spatially organized metabolic circuits.

Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances in computer vision, most of the currently used spot detection techniques are still based on classical signal processing and require tedious manual tuning per dataset. Here we introduce Spotiflow, a deep learning method for subpixel-accurate spot detection that formulates spot detection as a multiscale heatmap and stereographic flow regression problem. Spotiflow supports 2D and 3D images, generalizes across different imaging conditions and is more time and memory efficient than existing methods. We show the efficacy of Spotiflow by extensive quantitative experiments on diverse datasets and demonstrate that its increased accuracy leads to meaningful improvements in biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow.

The spatial arrangement of cells is fundamental to their function, but single-cell RNA sequencing dissociates cells from their native spatial context. We present LUNA, a generative AI model that reconstructs tissue architecture from dissociated gene expression profiles by learning spatial priors from spatially resolved transcriptomics datasets. We validate LUNA by reconstructing the MERFISH mouse brain atlas comprising over 1.2 million cells, inferring the spatial locations of dissociated cells in a mouse central nervous system single-cell RNA-sequencing atlas, and enriching the Slide-tags data by imputing the locations of nuclei lost during profiling. LUNA generalizes to previously unseen cell types in the MERFISH mouse brain atlas and to pathological samples, identifying regions of disease-associated alterations in a mouse model of Parkinson’s disease. Our results establish generative tissue reconstruction as an approach for overcoming current technological limitations in spatial biology and advancing our understanding of tissue organization and function.

Embryo development entails the formation of anatomical structures with distinct biochemical compositions. Compared with the wealth of knowledge on gene regulation, our understanding of metabolic programs operating during embryogenesis is limited. Mass spectrometry imaging (MSI) has the potential to map the distribution of metabolites across embryo development. Here, we established an analytical framework for the joint analysis of large MSI datasets that allows for the construction of multi-dimensional metabolomic atlases. Employing this framework, we mapped the 4D distribution of over a hundred lipids at quasi-single-cell resolution in Danio rerio embryos. We discovered metabolic trajectories that unfold in concert with morphogenesis and revealed spatially organized biochemical coordination overlooked by bulk measurements. Interestingly, lipid mapping revealed unexpected distributions of sphingolipid and triglyceride species, suggesting their involvement in pattern establishment and organ development. Our approach empowers a new generation of whole-organism metabolomic atlases and enables the discovery of spatially organized metabolic circuits.

Specialized signaling centers orchestrate robust development and regeneration. Limb morphogenesis, for instance, requires interactions between the mesoderm and the signaling center apical-ectodermal ridge (AER), whose properties and role in cell fate decisions have remained challenging to dissect. To tackle this, we developed mouse embryonic stem cells (mESCs)-based heterogeneous cultures and a limb organoid model, termed budoids, comprising cells with AER, surface ectoderm, and mesoderm properties. mESCs were first induced into heterogeneous cultures that self-organized into domes in 2D. Aggregating these cultures resulted in formation of limb bud-like structures in 3D, exhibiting chondrogenesis-based symmetry breaking and elongation. Using our organoids and quantitative in situ expression profiling, we uncovered that AER-like cells support nearby limb mesoderm and fibroblast identities while enhancing tissue polarization that permits distant cartilage formation. Together, our findings provide a powerful model to study aspects of limb morphogenesis, and reveal the ability of signaling center AER cells to concurrently modulate cell fate and spatial organization.

Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression. However, information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally-defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.

Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA variant frequencies. However, with rapidly increasing biobank sizes, these methods have become computationally intractable. Here we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as the current standard algorithm, ADMIXTURE, while reducing the compute time by orders of magnitude surpassing even the fastest alternatives. One month of continuous compute using ADMIXTURE can be reduced to just hours with Neural ADMIXTURE. A multi-head approach allows Neural ADMIXTURE to offer even further acceleration by computing multiple cluster numbers in a single run. Furthermore, the models can be stored, allowing cluster assignment to be performed on new data in linear time without needing to share the training samples.

The estimation of genetic clusters using genomic data has application from genome-wide association studies (GWAS) to demographic history to polygenic risk scores (PRS) and is expected to play an important role in the analyses of increasingly diverse, large-scale cohorts. However, existing methods are computationally-intensive, prohibitively so in the case of nationwide biobanks. Here we explore Archetypal Analysis as an efficient, unsupervised approach for identifying genetic clusters and for associating individuals with them. Such unsupervised approaches help avoid conflating socially constructed ethnic labels with genetic clusters by eliminating the need for exogenous training labels. We show that Archetypal Analysis yields similar cluster structure to existing unsupervised methods such as ADMIXTURE and provides interpretative advantages. More importantly, we show that since Archetypal Analysis can be used with lower-dimensional representations of genetic data, significant reductions in computational time and memory requirements are possible. When Archetypal Analysis is run in such a fashion, it takes several orders of magnitude less compute time than the current standard, ADMIXTURE. Finally, we demonstrate uses ranging across datasets from humans to canids.